Mixing and segregation assessment of bi-disperse solid particles in a double paddle mixer--颗粒学报PARTICUOLOGY

Jadidi, B., Ebrahimi, M., Ein-Mozaffari, F., & Lohi, A. (2023). Mixing and segregation assessment of bi-disperse solid particles in a double paddle mixer. Particuology, 74, 184-199. https://doi.org/10.1016/j.partic.2022.06.006

Mixing and segregation assessment of bi-disperse solid particles in a double paddle mixer

Behrooz Jadidi, Mohammadreza Ebrahimi, Farhad Ein-Mozaffari ^*, Ali Lohi

Department of Chemical Engineering, Ryerson University, 350 Victoria Street, Toronto, M5B 2K3, Canada

10.1016/j.partic.2022.06.006

Volume 74, March 2023, Pages 184-199

Received 15 March 2022, Revised 12 June 2022, Accepted 30 June 2022, Available online 16 July 2022, Version of Record 3 August 2022.

E-mail: fmozaffa@ryerson.ca

Highlights

• The blending of bi-disperse particles in a double paddle blender was examined.

• DEM parameters were calibrated using Plackett-Burman design experiment method.

• The effect of various operating parameters on mixing quality was investigated.

• Paddle speed and particle number ratio significantly affected the mixing quality.

• Diffusion was the dominant mixing mechanism in the double paddle blender.

Abstract

A double paddle blender's flow patterns and mixing mechanisms were analyzed using discrete element method (DEM) and experiments. The mixing performance of this type of the blender containing bi-disperse particles has been rarely studied in the literature. Plackett-Burman design of experiments (DoE) methodology was used to calibrate the DEM input parameters. Subsequently, the impact of the particle number ratio, vessel fill level, and paddle rotational speed on mixing performance was investigated using the calibrated DEM model. The mixing performance was assessed using relative standard deviation and segregation intensity. Mixing performance was significantly affected by the paddle rotational speed and particle number ratio. Moreover, the Peclet number and diffusivity coefficient were used to evaluate the mixing mechanism in the blender. Results revealed that the diffusion was the predominant mixing mechanism, and the best mixing performance was observed when the diffusivity coefficients of 3 mm and 5 mm particles were almost equal.

Graphical abstract

Keywords

Double paddle blender; Discrete element method (DEM); Granular mixing; Mixing kinetics and mechanism; Bi-disperse solid particles

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